1. Technical Field
The present invention relates to the field of video image processing, and more particularly, to a motion detection method, a motion detection apparatus and a passenger flow detection system.
2. Description of Related Art
In public transportation systems such as railway systems, metro systems and bus systems, by using an automatic passenger flow information statistical system to collect details about the direction, time, volume and distribution of passenger flow in individual routes and stations, it will be made easier for operators to make adjustment on the routes and arrange vehicle resources reasonably.
A conventional method of automatically making statistics of passenger flow information adopts an infrared (IR) interruption system and a pressure sensing system. When an object passes through this system, the IR ray will be interrupted. Accordingly, by counting the number of objects passing through the IR interruption system, statistics of the passenger flow can be obtained. However, this method cannot make statistics on the passenger flow in an accurate and timely way, especially in rush hours when there is a very large passenger flow; moreover, sites where such a system can be used are limited.
In contrast, images can carry richer and a larger volume of information, and owing to emergence and development of image processing technologies, a lot of new solutions to the problems confronted by the conventional passenger flow statistical technologies have been provided.
Currently, most of image processing methods for automatically making statistics of passenger flow utilize methods of feature recognition and pattern matching etc. in two-dimensional image processing. However, such methods are only applicable to situations where the background is relatively simple and fail to make a correct recognition if there are side-by-side objects or objects are crowded in succession. Therefore, motion detection technologies based on stereovision have now become a hot topic of research.
A prior art I provides a bus passenger flow statistical method based on stereovision. Referring to China Patent No. CN200510060288.2, there is provided a method for making statistics of passenger flow, which detect heads of persons by detecting distances from individual points in a scene to be detected to a video camera based on the monocular image feature recognition technology. As shown in FIG. 1, when round-like objects are extracted from a monocular image, a lot of false round objects will be obtained; then by removing the false round objects based on some criteria, each of the remaining round objects will correspond to a head of a person so that, by counting the number of the round objects, statistics of the passenger flow can be made.
A prior art II provides a method for determining a moving object by use of a motion detection technology. Referring to China Patent No. CN200710003278.4, the method primarily comprises: acquiring a depth image of a scene detected, establishing and initializing a Gaussian background model of the depth image, and determining pixel points of the moving object in the depth image according to the Gaussian background model.
However, there are still some problems with the prior arts. As an example, the prior art I only uses the depth information of the scene containing the object to be detected and relies on the two-dimensional feature recognition technology as a primary means. The depth information can only assist in removing the false round objects but cannot result in complete removal of the false round objects, so this method suffers from poor detection accuracy, leading to an inaccurate statistical result of the passenger flow. The method provided in the prior art II also only uses the depth information of a scene containing the object to be detected and necessitates use of a number of Gaussian statistics and determination model equations, which represents a considerable computational complexity; moreover, this method requires timely update of the Gaussian background models by using an algorithm. Unfortunately, in case of a dense passenger flow, the background will fail to be updated, making it impossible to detect the objects. Furthermore, in case of a dense passenger flow, this method will cause “conglutination” of a plurality of objects, making it impossible to divide detection regions of these objects and to make statistics of the objects.